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Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)
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099 - Don’t Boil the Ocean: How to Generate Business Value Early With Your Data Products with Jon Cooke, CTO of Dataception
Manage episode 340284946 series 2527129
Today I’m sitting down with Jon Cooke, founder and CTO of Dataception, to learn his definition of a data product and his views on generating business value with your data products. In our conversation, Jon explains his philosophy on data products and where design and UX fit in. We also review his conceptual model for data products (which he calls the data product pyramid), and discuss how together, these concepts allow teams to ship working solutions faster that actually produce value.
Highlights/ Skip to:
- Jon’s definition of a data product (1:19)
- Brian explains how UX research and design planning can and should influence data architecture —so that last mile solutions are useful and usable (9:47)
- The four characteristics of a data product in Jon’s model (16:16)
- The idea of products having a lifecycle with direct business/customer interaction/feedback (17:15)
- Understanding Jon’s data product pyramid (19:30)
- The challenges when customers/users don’t know what they want from data product teams - and who should be doing the work to surface requirements (24:44)
- Mitigating risk and the importance of having management buy-in when adopting a product-driven approach (33:23)
- Does the data product pyramid account for UX? (35:02)
- What needs to change in an org model that produces data products that aren’t delivering good last mile UXs (39:20)
Quotes from Today’s Episode
- “A data product is something that specifically solves a business problem, a piece of analytics, data use case, a pipeline, datasets, dashboard, that type that solves a business use case, and has a customer, and as a product lifecycle to it.” - Jon (2:15)
- “I’m a fan of any definition that includes some type of deployment and use by some human being. That’s the end of the cycle, because the idea of a product is a good that has been made, theoretically, for sale.” - Brian (5:50)
- “We don’t build a lot of stuff around cloud anymore. We just don’t build it from scratch. It’s like, you know, we don’t generate our own electricity, we don’t mill our own flour. You know, the cloud—there’s a bunch of composable services, which I basically pull together to build my application, whatever it is. We need to apply that thinking all the way through the stack, fundamentally.” - Jon (13:06)
- “It’s not a data science problem, it’s not a business problem, it’s not a technology problem, it’s not a data engineering problem, it’s an everyone problem. And I advocate small, multidisciplinary teams, which have a business value person in it, have an SME, have a data scientist, have a data architect, have a data engineer, as a small pod that goes in and answer those questions.” - Jon (26:28)
- “The idea is that you’re actually building the data products, which are the back-end, but you’re actually then also doing UX alongside that, you know? You’re doing it in tandem.” - Jon (37:36)
- “Feasibility is one of the legs of the stools. There has to be market need, and your market just may be the sales team, but there needs to be some promise of value there that this person is really responsible for at the end of the day, is this data product going to create value or not?” - Brian (42:35)
- “The thing about data products is sometimes you don’t know how feasible it is until you actually look at the data…You’ve got to do what we call data archaeology. You got to go and find the data, you got to brush it off, and you’re looking at and go, ‘Is it complete?’” - Jon (44:02)
113 قسمت
Manage episode 340284946 series 2527129
Today I’m sitting down with Jon Cooke, founder and CTO of Dataception, to learn his definition of a data product and his views on generating business value with your data products. In our conversation, Jon explains his philosophy on data products and where design and UX fit in. We also review his conceptual model for data products (which he calls the data product pyramid), and discuss how together, these concepts allow teams to ship working solutions faster that actually produce value.
Highlights/ Skip to:
- Jon’s definition of a data product (1:19)
- Brian explains how UX research and design planning can and should influence data architecture —so that last mile solutions are useful and usable (9:47)
- The four characteristics of a data product in Jon’s model (16:16)
- The idea of products having a lifecycle with direct business/customer interaction/feedback (17:15)
- Understanding Jon’s data product pyramid (19:30)
- The challenges when customers/users don’t know what they want from data product teams - and who should be doing the work to surface requirements (24:44)
- Mitigating risk and the importance of having management buy-in when adopting a product-driven approach (33:23)
- Does the data product pyramid account for UX? (35:02)
- What needs to change in an org model that produces data products that aren’t delivering good last mile UXs (39:20)
Quotes from Today’s Episode
- “A data product is something that specifically solves a business problem, a piece of analytics, data use case, a pipeline, datasets, dashboard, that type that solves a business use case, and has a customer, and as a product lifecycle to it.” - Jon (2:15)
- “I’m a fan of any definition that includes some type of deployment and use by some human being. That’s the end of the cycle, because the idea of a product is a good that has been made, theoretically, for sale.” - Brian (5:50)
- “We don’t build a lot of stuff around cloud anymore. We just don’t build it from scratch. It’s like, you know, we don’t generate our own electricity, we don’t mill our own flour. You know, the cloud—there’s a bunch of composable services, which I basically pull together to build my application, whatever it is. We need to apply that thinking all the way through the stack, fundamentally.” - Jon (13:06)
- “It’s not a data science problem, it’s not a business problem, it’s not a technology problem, it’s not a data engineering problem, it’s an everyone problem. And I advocate small, multidisciplinary teams, which have a business value person in it, have an SME, have a data scientist, have a data architect, have a data engineer, as a small pod that goes in and answer those questions.” - Jon (26:28)
- “The idea is that you’re actually building the data products, which are the back-end, but you’re actually then also doing UX alongside that, you know? You’re doing it in tandem.” - Jon (37:36)
- “Feasibility is one of the legs of the stools. There has to be market need, and your market just may be the sales team, but there needs to be some promise of value there that this person is really responsible for at the end of the day, is this data product going to create value or not?” - Brian (42:35)
- “The thing about data products is sometimes you don’t know how feasible it is until you actually look at the data…You’ve got to do what we call data archaeology. You got to go and find the data, you got to brush it off, and you’re looking at and go, ‘Is it complete?’” - Jon (44:02)
113 قسمت
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1 168 - 10 Challenges Internal Data Teams May Face Building Their First Revenue-Generating Data Product 38:24

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1 153 - What Impressed Me About How John Felushko Does Product and UX at the Analytics SAAS Company, LabStats 57:31

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1 150 - How Specialized LLMs Can Help Enterprises Deliver Better GenAI User Experiences with Mark Ramsey 52:22

1 149 - What the Data Says About Why So Many Data Science and AI Initiatives Are Still Failing to Produce Value with Evan Shellshear 50:18



1 146 - (Rebroadcast) Beyond Data Science - Why Human-Centered AI Needs Design with Ben Shneiderman 42:07

1 145 - Data Product Success: Adopting a Customer-Centric Approach With Malcolm Hawker, Head of Data Management at Profisee 53:09

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1 142 - Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod) 50:56


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1 137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen 44:50

1 136 - Navigating the Politics of UX Research and Data Product Design with Caroline Zimmerman 44:16

1 135 - “No Time for That:” Enabling Effective Data Product UX Research in Product-Immature Organizations 52:47




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1 130 - Nick Zervoudis on Data Product Management, UX Design Training and Overcoming Imposter Syndrome 48:56

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1 128 - Data Products for Dummies and The Importance of Data Product Management with Vishal Singh of Starburst 53:01

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1 121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill 39:40

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1 115 - Applying a Product and UX-Driven Approach to Building Stuart’s Data Platform with Osian Jones 45:19

1 114 - Designing Anti-Biasing and Explainability Tools for Data Scientists Creating ML Models with Josh Noble 42:05

1 113 - Turning the Weather into an Indispensable Data Product for Businesses with Cole Swain, VP Product at tomorrow.io 38:53

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1 107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives 42:52

1 106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley 44:14

1 105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need 41:53

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1 096 - Why Chad Sanderson, Head of Product for Convoy’s Data Platform, is a Champion of Data UX 37:36

1 095 - Increasing Adoption of Data Products Through Design Training: My Interview from TDWI Munich 16:50

1 094 - The Multi-Million Dollar Impact of Data Product Management and UX with Vijay Yadav of Merck 46:02



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1 085 - Dr. William D. Báez on the Journey and ROI of Integrating UX Design into Machine Learning and Analytics Solutions 44:42

1 084 - The Messy Truth of Designing and Building a Successful Analytics SAAS Product featuring Jonathan Kay (CEO, Apptopia) 39:56

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1 079 - How Sisu’s CPO, Berit Hoffmann, Is Approaching the Design of Their Analytics Product…and the UX Mistakes She Won’t Make Again 36:02

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